{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aPn5tGl5Yvo4"
   },
   "source": [
    "# Estimating causal effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 1782,
     "status": "ok",
     "timestamp": 1761515079826,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "srjLYpwJZKK2"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 585,
     "status": "ok",
     "timestamp": 1761515080413,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "rdjDiJwDY5K1",
    "outputId": "b31f458a-a38f-4fff-fad6-2263605494fa"
   },
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1761515080415,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "w0AdnbgkY1Q-"
   },
   "outputs": [],
   "source": [
    "# data_dir = '/content/drive/MyDrive/char_gender/data/'\n",
    "data_dir = '../data/'\n",
    "if not os.path.exists(data_dir):\n",
    "    os.makedirs(data_dir)\n",
    "fig_dir = '../figures/'\n",
    "if not os.path.exists(fig_dir):\n",
    "    os.makedirs(fig_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 859
    },
    "executionInfo": {
     "elapsed": 139,
     "status": "ok",
     "timestamp": 1761515080561,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "ZofPzlicY9j5",
    "outputId": "f424cd00-c05c-41d1-ba24-d57625773d6f"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Response ID</th>\n",
       "      <th>If you agree to these conditions, please click \"I consent to participate\" below. If you do not agree, click the “I do not consent to participate” option.\\n\\nBy agreeing to participate, you confirm that you are over 18 years of age.\\n\\n \\n\\nI have read the above information.</th>\n",
       "      <th>In Novel A, how long is the trail that Sam takes?</th>\n",
       "      <th>In Novel A, what plant does Sam rest against?</th>\n",
       "      <th>In Novel B, when does Alex go to the cafe?</th>\n",
       "      <th>In Novel B, what is the material of the chair Alex sketches?</th>\n",
       "      <th>chosen_story</th>\n",
       "      <th>Your opinion is fundamental to understand reader preferences. Please take your time to answer this question.\\n\\nIn your own words, briefly share your motivation for your choice (minimum 200 characters, or about 40 words).</th>\n",
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       "      <th>gender</th>\n",
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       "      <th>In Novel B, how long is the trail that Sam takes?</th>\n",
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       "      <td>Novel B</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>R_1IHhRxaGPtHjhQ2</td>\n",
       "      <td>I consent to participate.</td>\n",
       "      <td>6 miles</td>\n",
       "      <td>Juniper tree</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Wood</td>\n",
       "      <td>Novel A</td>\n",
       "      <td>I prefer to read novel A because it was easier...</td>\n",
       "      <td>25-35</td>\n",
       "      <td>Woman</td>\n",
       "      <td>...</td>\n",
       "      <td>Very liberal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>Hike</td>\n",
       "      <td>Hike</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R_1oFSEE8V2SpwrhD</td>\n",
       "      <td>I consent to participate.</td>\n",
       "      <td>6 miles</td>\n",
       "      <td>Juniper tree</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Wood</td>\n",
       "      <td>Novel B</td>\n",
       "      <td>I'm also interested in art and enjoy a main ch...</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Woman</td>\n",
       "      <td>...</td>\n",
       "      <td>Moderate</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
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       "      <td>I consent to participate.</td>\n",
       "      <td>6 miles</td>\n",
       "      <td>Juniper tree</td>\n",
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       "      <td>Novel A</td>\n",
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       "         Response ID  \\\n",
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       "3  R_1oFSEE8V2SpwrhD   \n",
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       "\n",
       "  If you agree to these conditions, please click \"I consent to participate\" below. If you do not agree, click the “I do not consent to participate” option.\\n\\nBy agreeing to participate, you confirm that you are over 18 years of age.\\n\\n \\n\\nI have read the above information.  \\\n",
       "0                          I consent to participate.                                                                                                                                                                                                                                   \n",
       "1                          I consent to participate.                                                                                                                                                                                                                                   \n",
       "2                          I consent to participate.                                                                                                                                                                                                                                   \n",
       "3                          I consent to participate.                                                                                                                                                                                                                                   \n",
       "4                          I consent to participate.                                                                                                                                                                                                                                   \n",
       "\n",
       "  In Novel A, how long is the trail that Sam takes?  \\\n",
       "0                                           6 miles   \n",
       "1                                               NaN   \n",
       "2                                           6 miles   \n",
       "3                                           6 miles   \n",
       "4                                           6 miles   \n",
       "\n",
       "  In Novel A, what plant does Sam rest against?  \\\n",
       "0                                  Juniper tree   \n",
       "1                                           NaN   \n",
       "2                                  Juniper tree   \n",
       "3                                  Juniper tree   \n",
       "4                                  Juniper tree   \n",
       "\n",
       "  In Novel B, when does Alex go to the cafe?  \\\n",
       "0                                    Morning   \n",
       "1                                        NaN   \n",
       "2                                    Morning   \n",
       "3                                    Morning   \n",
       "4                                    Morning   \n",
       "\n",
       "  In Novel B, what is the material of the chair Alex sketches? chosen_story  \\\n",
       "0                                               Wood                Novel A   \n",
       "1                                                NaN                Novel B   \n",
       "2                                               Wood                Novel A   \n",
       "3                                               Wood                Novel B   \n",
       "4                                               Wood                Novel A   \n",
       "\n",
       "  Your opinion is fundamental to understand reader preferences. Please take your time to answer this question.\\n\\nIn your own words, briefly share your motivation for your choice (minimum 200 characters, or about 40 words).  \\\n",
       "0  Novel A has more information about a potential...                                                                                                                                                                              \n",
       "1  Novel B drew me in with the suspense of exactl...                                                                                                                                                                              \n",
       "2  I prefer to read novel A because it was easier...                                                                                                                                                                              \n",
       "3  I'm also interested in art and enjoy a main ch...                                                                                                                                                                              \n",
       "4  My curiosity about the contents of the note/le...                                                                                                                                                                              \n",
       "\n",
       "  Your age: gender  ...  How would you describe your political views?  \\\n",
       "0     25-35    Man  ...                                      Moderate   \n",
       "1     36-45    Man  ...                                      Moderate   \n",
       "2     25-35  Woman  ...                                  Very liberal   \n",
       "3     18-24  Woman  ...                                      Moderate   \n",
       "4     36-45  Woman  ...                                       Liberal   \n",
       "\n",
       "  In Novel A, when does Alex go to the cafe?  \\\n",
       "0                                        NaN   \n",
       "1                                    Morning   \n",
       "2                                        NaN   \n",
       "3                                        NaN   \n",
       "4                                        NaN   \n",
       "\n",
       "  In Novel A, what is the material of the chair Alex sketches?  \\\n",
       "0                                                NaN             \n",
       "1                                               Wood             \n",
       "2                                                NaN             \n",
       "3                                                NaN             \n",
       "4                                                NaN             \n",
       "\n",
       "  In Novel B, how long is the trail that Sam takes?  \\\n",
       "0                                               NaN   \n",
       "1                                           6 miles   \n",
       "2                                               NaN   \n",
       "3                                               NaN   \n",
       "4                                               NaN   \n",
       "\n",
       "  In Novel B, what plant does Sam rest against? first_gender first_story  \\\n",
       "0                                           NaN            W        Hike   \n",
       "1                                  Juniper tree            M      Coffee   \n",
       "2                                           NaN            W        Hike   \n",
       "3                                           NaN            W        Hike   \n",
       "4                                           NaN            W        Hike   \n",
       "\n",
       "  Treatment A chose_hike respondent_woman  \n",
       "0        Hike          1                0  \n",
       "1        Hike          1                0  \n",
       "2        Hike          1                1  \n",
       "3        Hike          0                1  \n",
       "4        Hike          1                1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public = pd.read_csv(os.path.join(data_dir, 'public_data.csv'))\n",
    "public.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 470
    },
    "executionInfo": {
     "elapsed": 551,
     "status": "ok",
     "timestamp": 1761515081113,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "dwe1pNNbZHMp",
    "outputId": "f66a379e-938a-43e2-d1f1-1c5169c3a9f5"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "ax = sns.barplot(public, x=\"gender\", y=\"chose_hike\", order=['Woman', 'Man'], hue='Treatment A', errorbar=('ci', 95))\n",
    "ax.set_ylim(0.5, 1)\n",
    "ax.grid(color='grey', axis='y', linestyle='-', linewidth=0.25, alpha=0.5)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles=handles, labels=['woman protagonist',\n",
    "                                   'man protagonist'], title='Treatment: Hike Story has a')\n",
    "\n",
    "for p in ax.patches:\n",
    "  height = p.get_height()\n",
    "  if height > 0.5:\n",
    "    ax.text(p.get_x() + p.get_width() / 2., 0.52, f'{int(round(height, 2) * 100)}%', ha='center', va='center', color='white', fontsize=10) #fontsize = 20\n",
    "\n",
    "\n",
    "plt.xlabel('Respondent Gender')\n",
    "plt.ylabel('Probability of Choosing Hike Story')#(r' $\\hat{\\pi}$ ', rotation=0)\n",
    "plt.savefig(os.path.join(fig_dir, f'pihats.png'), dpi = 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1761515081115,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "3gtbOG2sX3nN"
   },
   "outputs": [],
   "source": [
    "dicts = []\n",
    "for gender in ['Woman', 'Man']:\n",
    "\n",
    "  # print(gender.upper())\n",
    "  subgroup = public[public['gender'] == gender].copy()\n",
    "  pihat1 = np.mean(subgroup[subgroup['Treatment A'] == 'Hike']['chose_hike'])\n",
    "  pivar1 = pihat1 * (1 - pihat1)/len(subgroup[subgroup['Treatment A'] == 'Hike'])\n",
    "\n",
    "  pihat0 = np.mean(subgroup[subgroup['Treatment A'] == 'Other']['chose_hike'])\n",
    "  pivar0 = pihat0 * (1 - pihat0)/len(subgroup[subgroup['Treatment A'] == 'Other'])\n",
    "\n",
    "  effect = pihat1-pihat0\n",
    "  effect_var = pivar1+pivar0\n",
    "  effect_se = np.sqrt(effect_var)\n",
    "\n",
    "  cimin = effect - stats.norm.ppf(.975)*effect_se\n",
    "  cimax = effect + stats.norm.ppf(.975)*effect_se\n",
    "  d = {'Respondent Gender': gender,\n",
    "       'effect': effect,\n",
    "       'cimin': cimin,\n",
    "       'cimax': cimax}\n",
    "  dicts.append(d)\n",
    "\n",
    "  # print(f'pi hat1 {pi_hat1}', f'pi hat0 {pi_hat0}', f'pi hat {pi_hat}')\n",
    "#print(f'Average treatment effect: {np.average(effects, weights=weights)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 125
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1761515081135,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "0fXY2pLtZ-pI",
    "outputId": "3abe1975-470d-4703-bf99-5a427da79011"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Respondent Gender</th>\n",
       "      <th>effect</th>\n",
       "      <th>cimin</th>\n",
       "      <th>cimax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Woman</td>\n",
       "      <td>0.061584</td>\n",
       "      <td>0.016927</td>\n",
       "      <td>0.106240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Man</td>\n",
       "      <td>0.007931</td>\n",
       "      <td>-0.035845</td>\n",
       "      <td>0.051707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Respondent Gender    effect     cimin     cimax\n",
       "0             Woman  0.061584  0.016927  0.106240\n",
       "1               Man  0.007931 -0.035845  0.051707"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(dicts)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1761515081145,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "jr61eiAcaJbL",
    "outputId": "b96b240f-b512-40a8-e391-0734784a1720"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.06158358, 0.0079309 ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['effect'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 379
    },
    "executionInfo": {
     "elapsed": 129,
     "status": "ok",
     "timestamp": 1761515081301,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "ExoOeigsaM4s",
    "outputId": "5c93d775-2091-409e-e0dd-1d8c7b24a82e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3, 3))\n",
    "plt.xlim(0,2)\n",
    "plt.ylim(-0.1, 0.1)\n",
    "plt.grid(color='grey', axis='y', linestyle='-', linewidth=0.25, alpha=0.5)\n",
    "for pos in ['top', 'right']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "\n",
    "asymmetric_errors = []\n",
    "for index, row in df.iterrows():\n",
    "  asymmetric_errors.append([row['effect'] - row['cimin'], row['cimax'] - row['effect']])\n",
    "plt.errorbar([0.5,1.5], df['effect'].values, yerr=asymmetric_errors, color='black', fmt='o')\n",
    "\n",
    "for x, y in zip([0.6, 1.6], df['effect'].values):\n",
    "  plt.text(x+0.01, y, f'{round(y, 3)}%', ha='left', va='bottom', fontsize=10, color='black')\n",
    "\n",
    "plt.xticks([0.5, 1.5], labels=['Woman', 'Man'])\n",
    "plt.yticks(np.arange(-0.1, 0.11, step=0.05))\n",
    "for y in np.arange(-0.1, 0.11, step=0.025):\n",
    "    plt.axhline(y=y, color='grey', linestyle='-', linewidth=0.25, alpha=0.2)\n",
    "\n",
    "plt.axhline(y=0, color='black', linestyle='-', linewidth=0.5)\n",
    "\n",
    "plt.xlabel('Respondent Gender', labelpad = 8)\n",
    "plt.ylabel('Effect of Protagonist Gender\\n(Woman - Man)\\non Reader Preferences')\n",
    "plt.title('Conditional Average\\nTreatment Effect', pad=20)\n",
    "plt.savefig(os.path.join(fig_dir, f'cate.png'), dpi = 300, bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-RP6pX5KXeu_"
   },
   "source": [
    "## Bootstrapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 8252,
     "status": "ok",
     "timestamp": 1761515089552,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "19WeyMRDsTC8"
   },
   "outputs": [],
   "source": [
    "# Bootstrap calculations\n",
    "trials = 1000\n",
    "\n",
    "bootstrapped_ate = []\n",
    "bootstrapped_pihat = {'Woman': [],\n",
    "                      'Man': []}\n",
    "\n",
    "for i in range(trials):\n",
    "\n",
    "  effects = []\n",
    "  weights = []\n",
    "\n",
    "  for gender in ['Woman', 'Man']:\n",
    "\n",
    "    subgroup = public[public['gender'] == gender].copy()\n",
    "\n",
    "    A1 = subgroup[subgroup['Treatment A'] == 'Hike']\n",
    "    k = len(A1)\n",
    "    A1_sample = A1.sample(n=k, replace=True)\n",
    "\n",
    "    A2 = subgroup[subgroup['Treatment A'] == 'Other']\n",
    "    k = len(A2)\n",
    "    A2_sample = A2.sample(n=k, replace=True)\n",
    "\n",
    "    pi_hat1 =  np.mean(A1_sample['chose_hike'])\n",
    "    pi_hat0 =  np.mean(A2_sample['chose_hike'])\n",
    "    pi_hat = pi_hat1-pi_hat0\n",
    "\n",
    "    bootstrapped_pihat[gender].append(pi_hat)\n",
    "    effects.append(pi_hat)\n",
    "    weights.append(len(subgroup))\n",
    "\n",
    "  average_effect = np.average(effects, weights=weights)\n",
    "  bootstrapped_ate.append(average_effect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 51,
     "status": "ok",
     "timestamp": 1761515089604,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "GuGZN_qTwOy4"
   },
   "outputs": [],
   "source": [
    "def calculate_CI(bootstrapped):\n",
    "\n",
    "  sort = sorted(bootstrapped)\n",
    "  low = round(float(sort[int(trials*0.025)]), 5)\n",
    "  high = round(float(sort[int(trials*0.975)]), 5)\n",
    "\n",
    "  return low, high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1761515089605,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "qNa4IrDqx-65",
    "outputId": "1d105629-9664-45f7-b3fe-7c965c0f8db4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 Bootstrapped pihat for Woman: (0.01872, 0.10713)\n",
      "1000 Bootstrapped pihat for Man: (-0.03746, 0.05004)\n"
     ]
    }
   ],
   "source": [
    "for k, v in bootstrapped_pihat.items():\n",
    "  low, high = calculate_CI(v)\n",
    "  print(f'{trials} Bootstrapped pihat for {k}: ({low}, {high})')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RdVeTHSZes2h"
   },
   "source": [
    "# Analyzing demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 51,
     "status": "ok",
     "timestamp": 1761515096772,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "S0Mon3M6esol",
    "outputId": "850cb303-1621-47d7-af8f-9eae8b5afcf6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Response ID',\n",
       "       'If you agree to these conditions, please click \"I consent to participate\" below. If you do not agree, click the “I do not consent to participate” option.\\n\\nBy agreeing to participate, you confirm that you are over 18 years of age.\\n\\n \\n\\nI have read the above information.',\n",
       "       'In Novel A, how long is the trail that Sam takes?',\n",
       "       'In Novel A, what plant does Sam rest against?',\n",
       "       'In Novel B, when does Alex go to the cafe?',\n",
       "       'In Novel B, what is the material of the chair Alex sketches?',\n",
       "       'chosen_story',\n",
       "       'Your opinion is fundamental to understand reader preferences. Please take your time to answer this question.\\n\\nIn your own words, briefly share your motivation for your choice (minimum 200 characters, or about 40 words).',\n",
       "       'Your age:', 'gender', 'Your gender - Prefer to self describe: - Text',\n",
       "       'What was your total family income before taxes during the past 12 months?',\n",
       "       'List of Countries', 'Do you currently reside in the United States?',\n",
       "       'What is your current United States Zip Code?',\n",
       "       'How would you describe your political views?',\n",
       "       'In Novel A, when does Alex go to the cafe?',\n",
       "       'In Novel A, what is the material of the chair Alex sketches?',\n",
       "       'In Novel B, how long is the trail that Sam takes?',\n",
       "       'In Novel B, what plant does Sam rest against?', 'first_gender',\n",
       "       'first_story', 'Treatment A', 'chose_hike', 'respondent_woman'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 214
    },
    "executionInfo": {
     "elapsed": 49,
     "status": "ok",
     "timestamp": 1761516446847,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "0bcee2e7",
    "outputId": "14a33a1b-487f-4194-96d7-4b45b162beb4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Political View</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Very conservative</td>\n",
       "      <td>5.499665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Conservative</td>\n",
       "      <td>16.130114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Moderate</td>\n",
       "      <td>27.162978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Liberal</td>\n",
       "      <td>33.266264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Very liberal</td>\n",
       "      <td>17.940979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Political View  Percentage\n",
       "4  Very conservative    5.499665\n",
       "3       Conservative   16.130114\n",
       "1           Moderate   27.162978\n",
       "0            Liberal   33.266264\n",
       "2       Very liberal   17.940979"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "political_views_counts = public['How would you describe your political views?'].value_counts()\n",
    "political_views_percentages = political_views_counts / political_views_counts.sum() * 100\n",
    "\n",
    "political_views_table = pd.DataFrame({\n",
    "    'Political View': political_views_percentages.index,\n",
    "    'Percentage': political_views_percentages.values\n",
    "})\n",
    "\n",
    "# Optional: Define an order for political views if needed for sorting\n",
    "political_views_order = [\n",
    "    'Very conservative',\n",
    "    'Conservative',\n",
    "    'Moderate',\n",
    "    'Liberal',\n",
    "    'Very liberal',\n",
    "    'Prefer not to disclose'\n",
    "]\n",
    "political_views_table['Political View'] = pd.Categorical(political_views_table['Political View'], categories=political_views_order, ordered=True)\n",
    "political_views_table = political_views_table.sort_values('Political View')\n",
    "\n",
    "\n",
    "display(political_views_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 246
    },
    "executionInfo": {
     "elapsed": 50,
     "status": "ok",
     "timestamp": 1761516359344,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "50eb47e5",
    "outputId": "90b181d3-31a7-4574-f8c9-9d6019faf004"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income Level</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prefer not to disclose</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Less than $25,000</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$25,000-$49,999</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$50,000-$99,999</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$100,000-$199,999</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>More than $200,000</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Income Level  Percentage\n",
       "5  Prefer not to disclose         2.0\n",
       "3       Less than $25,000        11.0\n",
       "2         $25,000-$49,999        20.0\n",
       "0         $50,000-$99,999        37.0\n",
       "1       $100,000-$199,999        25.0\n",
       "4      More than $200,000         6.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "income_counts = public['What was your total family income before taxes during the past 12 months?'].value_counts()\n",
    "income_percentages = round(income_counts / income_counts.sum() * 100)\n",
    "\n",
    "income_table = pd.DataFrame({\n",
    "    'Income Level': income_percentages.index,\n",
    "    'Percentage': income_percentages.values\n",
    "})\n",
    "\n",
    "# Optional: Reorder the table according to the defined income_order\n",
    "income_order = [\n",
    "    'Prefer not to disclose',\n",
    "    'Less than $25,000',\n",
    "    '$25,000-$49,999',\n",
    "    '$50,000-$99,999',\n",
    "    '$100,000-$199,999',\n",
    "    'More than $200,000'\n",
    "]\n",
    "income_table['Income Level'] = pd.Categorical(income_table['Income Level'], categories=income_order, ordered=True)\n",
    "income_table = income_table.sort_values('Income Level')\n",
    "\n",
    "\n",
    "display(income_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 807
    },
    "executionInfo": {
     "elapsed": 634,
     "status": "ok",
     "timestamp": 1761515427987,
     "user": {
      "displayName": "Federica Bologna",
      "userId": "16530074403271623482"
     },
     "user_tz": 240
    },
    "id": "a-thvQhte0n-",
    "outputId": "0a9673b0-c4a9-4ea2-9cdc-7c6e9a1231ca"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "income_order = [\n",
    "    'Prefer not to disclose',\n",
    "    'Less than $25,000',\n",
    "    '$25,000-$49,999',\n",
    "    '$50,000-$99,999',\n",
    "    '$100,000-$199,999',\n",
    "    'More than $200,000'\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "sns.countplot(data=public, x='What was your total family income before taxes during the past 12 months?', order=income_order, palette='colorblind', hue='What was your total family income before taxes during the past 12 months?', legend=False)\n",
    "plt.title('Distribution of Family Income')\n",
    "plt.xlabel('Total Family Income Before Taxes (Past 12 Months)')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "RTUmgRLafN5p"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Your age:\n",
       "25-35    0.330204\n",
       "36-45    0.251425\n",
       "46-55    0.189742\n",
       "56-65    0.096212\n",
       "18-24    0.076768\n",
       "65+      0.055649\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public[\"Your age:\"].value_counts() / public[\"Your age:\"].value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPbsAsLnbQlvOtohjUMAeL+",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "nlp25",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
